Beidou-based transmission tower line integrated monitoring device and monitoring method

Abstract

The invention discloses a Beidou-based power transmission tower line integrated monitoring device and a Beidou-based power transmission tower line integrated monitoring method. The Beidou-based power transmission tower line integrated monitoring device comprises an integrated monitoring front end, a line monitoring group, a tower monitoring group and a data display platform, the line monitoring group is used for monitoring the power transmission line in all directions and consists of a plurality of line sensors for monitoring the state of the power transmission line; the tower monitoring group is used for monitoring the posture of the transmission tower and the environmental factors of the tower; the integrated monitoring front end is used for performing integrated analysis on the monitoring data of the power transmission line monitoring group and the power transmission tower monitoring group, evaluating the attitude of the current tower and the power transmission line, and uploading the current state data of the power transmission tower and the power transmission line and the early warning level to the data display platform. And the data display platform is used for displaying the current states of the power transmission tower and the power transmission line and displaying whether a state alarm exists or not. The method is applied to the technical field of power transmission monitoring.

Description

Technical Field

[0001] The present invention is applied to the technical field of power transmission monitoring, and particularly relates to a Beidou-based power transmission tower line integrated monitoring device and monitoring method. Background Art

[0002] With the rapid development of the national economy, the demand for electricity in all walks of life is increasing, and the requirements for the quality stability, uninterruptedness and accompanying services of the power supply provided by the power supply department are also getting higher and higher. Therefore, the safety of the power grid operation of long-distance high-voltage transmission lines is particularly important.

[0003] In recent years, severe weather has occurred frequently, and extreme weather such as heavy rain and heavy snowfall can damage the safety of lines and towers. By monitoring and analyzing the status of conductors and towers, possible failures of transmission towers and their transmission lines can be predicted in advance and corresponding measures can be taken in advance to avoid major economic losses.

[0004] Therefore, it is particularly important to adopt effective methods to monitor transmission towers and lines in real time, issue early warnings on the status of transmission lines in a timely manner, and take corresponding measures.

[0005] Current transmission line monitoring has shortcomings:

[0006] 1. Manual inspection is time-consuming and labor-intensive, and the effect cannot be quantified; 2. Various monitoring systems are scattered, each system monitors one type of data, and the monitoring data type is single, which makes it impossible to comprehensively analyze the operating status of towers and lines. 3. The clocks of various monitoring data are not synchronized, and the upload intervals are asynchronous. There is a possibility of analysis errors due to the data not being sent at the same time.

[0007] In addition, with the continuous growth of electricity demand, the safety and stability of transmission lines have become increasingly important. As an important supporting structure for power transmission, the inclination and settlement of transmission line towers directly affect the safe operation of the power system. Therefore, the inclination and settlement monitoring of transmission line towers is also particularly important. At present, the inclination and settlement monitoring of transmission line towers is monitored by a single sensor, which is prone to insufficient monitoring accuracy. Summary of the invention

[0008] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a Beidou-based transmission tower line integrated monitoring device and monitoring method that solves the difficulties in evaluating the operating status of transmission tower lines, the problem that various types of monitoring data cannot be aggregated for unified early warning, and the fusion of multiple algorithms can improve the accuracy of monitoring the inclination and settlement status of towers.

[0009] The technical solution adopted by the present invention is: the present invention includes a Beidou-based transmission tower line integrated monitoring device and monitoring method, the Beidou-based transmission tower line integrated monitoring device includes an integrated monitoring front end, a line monitoring group, a tower monitoring group and a data display platform;

[0010] The line monitoring group is used to perform all-round monitoring of the transmission line, and is composed of a number of line sensors for monitoring the status of the transmission line;

[0011] The tower monitoring group is used to monitor the posture of the transmission tower itself and the tower environmental factors;

[0012] The integrated monitoring front end is responsible for integrating and analyzing the monitoring data of the transmission line monitoring group and the transmission tower monitoring group, evaluating the posture of the current towers and transmission lines, and sending the current transmission tower and transmission line status data and warning levels to the data display platform;

[0013] The data display platform is used to display the current status of transmission towers and transmission lines, and to show whether there are status alarms.

[0014] Furthermore, the line sensors include conductor temperature monitoring sensors, conductor dance monitoring sensors, tension clamp temperature monitoring sensors, and conductor current monitoring sensors; the pole tower monitoring group includes pole tower tilt monitoring sensor modules, micro-meteorological monitoring sensors, and monitoring cameras, and the micro-meteorological monitoring sensors are seven-element micro-meteorological sensors, wherein the seven elements are ambient temperature, ambient humidity, wind speed, wind direction, rainfall, light radiation intensity, and atmospheric pressure.

[0015] Furthermore, the sensors and monitoring cameras in the line monitoring group and the pole tower monitoring group all include a Beidou positioning module, and the Beidou positioning module is used to perform Beidou satellite time calibration on all sensors and monitoring cameras to keep all sensors and monitoring cameras in the same clock system.

[0016] Furthermore, the tower inclination monitoring sensor module includes a millimeter wave radar module responsible for collecting the distance from the device to the ground, a first six-axis acceleration sensor responsible for collecting the three-axis acceleration and three-axis angular velocity of the device, a barometer module responsible for collecting atmospheric pressure, a high-precision positioning module responsible for collecting longitude and latitude data and altitude, a three-axis geomagnetic module responsible for collecting geomagnetic declination data, and a charge and discharge management system responsible for collecting the charging current, charging voltage, discharge current and battery voltage of the device.

[0017] Furthermore, the monitoring camera includes a camera processor module, an AI module, a 4G module, a wireless communication module and a second six-axis acceleration sensor; the monitoring camera uses the wireless communication module to collect data from the pole tower tilt monitoring sensor module, combines the on-site image, the second six-axis acceleration sensor and the wireless pole tower tilt sensor module data inside the camera processor module, uses the AI ​​module to perform deep learning calculations, uploads the on-site data to the integrated monitoring front end and sends the analysis results to the data display platform.

[0018] Furthermore, a monitoring method based on a Beidou-based transmission tower line integrated monitoring device includes Beidou satellite time calibration for all sensors and monitoring cameras through a Beidou positioning module, followed by comprehensive temperature judgment monitoring of the transmission line and tilt settlement monitoring of the transmission tower pole, and the monitoring results are divided into five warning levels, namely, level 1: normal state; level 2: attention; level 3: close attention; level 4: general abnormality; level 5: serious abnormality;

[0019] The comprehensive judgment and monitoring method for the temperature of the transmission line includes the following steps:

[0020] A1. The temperature of the transmission conductor is collected by the conductor temperature monitoring sensor, the temperature of the tension clamp is collected by the tension clamp temperature monitoring sensor, the current of the transmission conductor is collected by the conductor current monitoring sensor, and the ambient temperature and light radiation intensity are collected by the micro-meteorological monitoring sensor. The data integration analysis is processed in the integrated monitoring front end;

[0021] A2. Set the temperature alarm threshold to 45°C. When the tension clamp temperature exceeds the limit alarm, the following process will be used for judgment:

[0022] A21. If the temperature of the tension clamp is greater than 45°C, set the warning level to Level 2, Concern;

[0023] A22. Sample the temperature data of the transmission line. If the temperature data of the transmission line is less than or equal to 45°C, the temperature of the tension clamp alone is abnormally high. It is judged that the tension clamp is abnormal and needs to be checked urgently. The warning status is set to level 4;

[0024] A23. If the temperature data of the transmission line is greater than 45°C, the transmission line current and ambient temperature data are sampled, and the theoretical value of temperature change is calculated based on the axial line heat dissipation model. If the theoretical value of temperature change is greater than 0, it is determined that the line temperature has increased due to the line current and will continue to increase, and the warning level is set to level 2. If the theoretical value of temperature change is less than or equal to 0, the line temperature change is unrelated to the line current, and the next step is to make a judgment based on the light radiation intensity.

[0025] The above wire heat dissipation model is as follows:

[0026]

[0027] in

[0028] ΔT: temperature change; I: conductor current; ρ: conductor resistivity; α: conductor resistance temperature coefficient; θ: conductor temperature; K t : comprehensive heat dissipation coefficient; L: conductor cross-sectional perimeter; S: conductor cross-sectional area;

[0029] If the light radiation intensity is greater than 100W / m2, the light radiation intensity will affect the temperature changes of the conductor and the tension clamp temperature. The warning level is set to level 3 and close attention is required. If the light radiation intensity is not greater than 100W / m2, the temperature change may be caused by discharge caused by foreign objects hanging on the conductor, etc. The warning level is set to level 4 and the abnormality is reported.

[0030] Further, the Beidou satellite time correction is a clock system based on the Beidou satellite, and the above-mentioned sensor acquisition data message includes a timestamp. The integrated monitoring front end receives the sensor message, verifies and records the timestamp of the data, and the above-mentioned sensor message format: message header (1 byte) + timestamp (4 bytes, century seconds) + monitoring data + check bit (2 bytes, CRC16-MODBUS) + message tail (1 byte); wherein the integrated front-end device receives the message sent by the sensor, verifies the timestamp, and if the timestamp in the sensor message differs from its own time by more than or equal to 3 seconds, the current monitoring data is discarded and the time correction logic is performed; if the difference is less than 3 seconds, it is considered that the time is normal, and the timestamp and monitoring data in the message are saved for subsequent comprehensive analysis logic use;

[0031] Input the time t1 of the integrated monitoring front-end device and the time t2 in the monitoring message, take t1 and t2 as the initial values, establish the natural time clocks T1 and T2, which are used for subsequent time comparison. Through the timing function of the Beidou positioning module, introduce the standard clock source and create the natural time clock T3, compare T1 and T2 with T3 respectively. If the difference between the clock T1 of the integrated monitoring front-end device and the standard clock T3 is greater than or equal to 3 seconds, the clock of the integrated monitoring front-end device is abnormal, and the standard clock T3 is used for correction. If the difference between the monitoring message clock T2 and the standard clock is greater than or equal to 3 seconds, the sensor clock is abnormal, and the integrated monitoring front-end device will send instructions to the sensor to let it calibrate the time through the Beidou positioning module;

[0032] When the clock correction is completed, the integrated monitoring front-end device sends instructions to the sensor, and the sensor needs to re-send the monitoring data.

[0033] Furthermore, the method for monitoring the inclination and settlement of a transmission tower comprises the following steps:

[0034] S1. Set the tower tilt and settlement monitoring cycle and send it to the monitoring camera. The monitoring camera is in standby mode most of the time. When the monitoring cycle time arrives, the monitoring camera first sends the tilt and settlement monitoring command to the tower tilt monitoring sensor module through the wireless communication module. The monitoring camera and the tower tilt monitoring sensor module collect and analyze the transmission tower tilt and settlement monitoring data. The specific steps are as follows:

[0035] S11. Start the initialization of the second six-axis acceleration sensor, collect three-axis acceleration and three-axis angular velocity for 10 cycles, and use the complementary filtering algorithm and Kalman filtering algorithm to calculate the attitude angle of the collected data to obtain the current attitude angle. The overall error percentage formula for the attitude angle is as follows:

[0036] Overall attitude angle error = |X current - X initial / user preset maximum offset value of X-axis inclination | + |Y current - Y initial / user preset maximum offset value of Y-axis inclination | / 2*100

[0037] Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default;

[0038] S12. The monitoring camera controls the camera processor module to run to the calibration preset position, collects a static picture, uses the AI ​​module to use the VGG neural network pre-trained at the factory to extract the features of the collected static picture, and compares the calibration picture features with the calibration picture features through the cosine similarity method to obtain the dissimilarity rate between the two, thereby obtaining the error percentage of the picture feature quantity; the overall error of the tilt settlement of the monitoring camera can be obtained by the following formula:

[0039] The overall error of the tilt and settlement of the monitoring camera = K1* overall error of the attitude angle + K2* error of the image feature quantity

[0040] Among them, K1 is the weight of the overall error of the attitude angle, accounting for 80%, K2 is the weight of the error of the image feature quantity, accounting for 20%, K1+K2=1; in the monitoring camera system, the monitoring of the attitude angle is the most effective way to reflect whether the transmission tower has tilted or settled, and the error of the image feature quantity is used to assist in monitoring whether the transmission tower has settled;

[0041] S2. The first step of the pole tower tilt monitoring sensor module is to start the first six-axis acceleration sensor to collect 10 cycles of three-axis acceleration and three-axis angular velocity, and use the complementary filtering algorithm and Kalman filtering algorithm to solve the attitude angle of the collected data to obtain the current attitude angle of the pole tower tilt monitoring sensor module. The overall error percentage formula of the attitude angle of the pole tower tilt monitoring sensor module is calculated as follows:

[0042] Overall attitude angle error = |X current - X initial / user preset maximum offset value of X-axis inclination | + |Y current - Y initial / user preset maximum offset value of Y-axis inclination | / 2*100

[0043] Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default;

[0044] The second step is to start the millimeter wave radar module for uninterrupted measurement, collect the distance to the ground for 10 cycles, add the Kalman filter algorithm to obtain the current distance to the ground of the tower tilt monitoring sensor module, and calculate the error percentage formula of the tower tilt monitoring sensor module to the ground as follows:

[0045] Ground distance error = |Current ground distance - Initial ground distance / User preset maximum ground distance error value | * 100

[0046] The maximum value of the ground distance error preset by the user is 10 cm by default;

[0047] The third step is to enable the barometer module to collect atmospheric pressure for 5 cycles, use the mean algorithm to calculate the atmospheric pressure at the height of the tower tilt monitoring sensor module, and then use the formula h=p0-p / k to calculate the current vertical height of the tower tilt monitoring sensor module. The formula for calculating the vertical height error percentage of the tower tilt monitoring sensor module is as follows:

[0048] Vertical height error = |Current vertical height - Initial vertical height / Maximum vertical height error preset by the user| * 100

[0049] The maximum vertical height error preset by the user is 10 cm by default;

[0050] The fourth step is to enable the three-axis geomagnetic module to read the magnetic field strength in the X, Y, and Z directions, use the formula to calculate the heading angle, and then use the formula to convert the previously calculated heading angle into degrees. The converted degrees are the current heading angle of the tower tilt monitoring sensor module. The formula for calculating the heading angle error percentage of the tower tilt monitoring sensor module is as follows:

[0051] Heading angle error = |current heading angle - initial heading angle / maximum heading angle error preset by the user | * 100

[0052] The default maximum value of the heading angle error preset by the user is 5 degrees;

[0053] The fifth step is to enable the high-precision positioning module and wait for the high-precision positioning module to complete the cold start and enter the positioning state. The high-precision antenna obtains multiple Beidou satellite signals to complete single-point positioning. The wireless communication module forwards the data packet containing the BDGGA data frame required by Qianxun Positioning to the monitoring camera. The monitoring camera successfully authenticates with the Qianxun Positioning background and obtains the differential data packet of the RTCM3.2 protocol. It is forwarded to the processor of the pole tower tilt monitoring sensor module through the wireless communication module at a frequency of 1Hz. The processor then uses the TTL interface to forward it to the high-precision positioning module. The high-precision positioning module performs differential centimeter-level positioning and uses the Kalman filter algorithm to filter the longitude and latitude and altitude data to obtain higher-precision longitude and altitude data; the longitude and latitude and altitude data of this positioning are used as the current longitude and latitude and altitude of the pole tower tilt monitoring sensor module; the formula for calculating the latitude and longitude altitude error percentage of the pole tower tilt monitoring sensor module is as follows:

[0054] Earth radius: The average radius of the earth is usually taken as R, which is 6371 km. The longitude and latitude are converted into radians:

[0055] lat1=radians(lat1),lon1=radians(lon1)

[0056] Lat2=radians(lat2),lon2=radians(lon2)

[0057] Use the following formula to calculate the current high-precision positioning and initial high-precision positioning three-dimensional space coordinates (x, y, z):

[0058] x=(R+h)×cos(lat)×cos(lon)

[0059] y=(R+h)×cos(lat)×sin(lon)

[0060] z=(R+h)×sin(lat)

[0061] where h is the height above sea level.

[0062] 3D distance calculation

[0063] After obtaining the three-dimensional coordinates of the current high-precision positioning and the initial high-precision positioning, use the three-dimensional space distance formula:

[0064]

[0065] Latitude and longitude altitude error = |spatial distance d - maximum value of user preset safety distance / maximum value of user preset safety distance | * 100

[0066] The maximum value of the user-preset safety distance is 10 cm by default

[0067] The overall error of the tilt settlement of the pole tower tilt monitoring sensing module can be obtained through the following formula:

[0068] The overall error of the tilt settlement of the pole tower tilt monitoring sensing module = L1 * overall attitude angle error + L2 * ground distance error + L3 * vertical height error + L4 * heading angle error + L5 * longitude and latitude altitude error

[0069] Among them, L1 is the weight of the overall attitude angle error, with a proportion of 51%, L2 is the weight of the ground distance error, with a proportion of 9%, L3 is the weight of the vertical height error, with a proportion of 9%, L4 is the weight of the heading angle error, with a proportion of 5%, L5 is the weight of the longitude and latitude altitude error, with a proportion of 26%, and L1 + L2 + L3 + L4 + L5 = 1; Through self-test experiments combined with the sensor usage environment, it is found that the attitude angle has a higher accuracy for the tilt monitoring of transmission pole towers than other parameters. Secondly, it is the longitude and latitude altitude. The ground distance, vertical height, and heading angle play an auxiliary monitoring role;

[0070] S3. The pole tower tilt monitoring sensing module reports the overall error result of the tilt settlement to the monitoring camera through the wireless communication module. The monitoring camera collates its own overall error of the tilt settlement and uploads it to the integrated monitoring front end through the 4G module. The integrated monitoring front end calculates the warning level of the transmission pole tower according to the preset weight ratio. The calculation method is as follows:

[0071] The tilt settlement error E of the transmission pole tower = J1 * the overall error of the tilt settlement of the monitoring camera + J2 * the overall error of the tilt settlement of the pole tower tilt monitoring sensing module

[0072] Among them, J1 is the weight of the monitoring camera, with a proportion of 28%, J2 is the weight of the pole tower tilt monitoring sensing module, with a proportion of 72%, and J1 + J2 = 1; In this method, the monitoring of the tilt settlement of the transmission pole tower is mainly completed by the wireless pole tower sensor, and the monitoring camera plays an auxiliary monitoring role. The weight ratio is obtained through laboratory environment testing; Among them, when 0 < E ≤ 1, it is in a normal state; 1 < E ≤ 2 is level 1, 2 < E ≤ 3 is level 2; 3 < E ≤ 4 is level 3; 4 < E ≤ 5 is level 4; E > 5 is level 5.

[0073] The beneficial effects of the present invention are: 1. Enhance the monitoring of the operation status of transmission pole towers and transmission lines, achieve early warning and early elimination of potential hazards, thereby improving the reliability of the power grid operation; 2. A pole tower tilt settlement monitoring method that combines multiple algorithms. The pole tower tilt monitoring sensing module uses a combination of multiple sensors to calculate and analyze the pole tower tilt settlement result, which is combined with the pole tower tilt settlement result obtained by the monitoring camera using AI image analysis, solving the problem of insufficient monitoring accuracy by a single sensor in the prior art. Description of the Drawings

[0074] Figure 1 It is a schematic diagram of the present invention;

[0075] Figure 2 This is the schematic diagram of the tower tilt monitoring sensor module;

[0076] Figure 3 This is the schematic diagram of the surveillance camera;

[0077] Figure 4 It is a time correction logic flow chart diagram;

[0078] Figure 5 It is a logic flow chart for the comprehensive judgment application of conductor temperature;

[0079] Figure 6 It is a logic flow chart of wire dancing warning judgment;

[0080] Figure 7 It is a logical flow chart of early warning of wire dancing. DETAILED DESCRIPTION

[0081] like Figure 1 As shown, in this embodiment, the present invention includes a Beidou-based transmission tower line integrated monitoring device and a monitoring method, wherein the Beidou-based transmission tower line integrated monitoring device includes an integrated monitoring front end 1, a line monitoring group 2, a tower monitoring group 3 and a data display platform 4;

[0082] The line monitoring group 2 is used to perform all-round monitoring of the transmission line, and is composed of a number of line sensors for monitoring the status of the transmission line;

[0083] The tower monitoring group 3 is used to monitor the posture of the transmission tower itself and the tower environmental factors;

[0084] The integrated monitoring front end 1 is responsible for integrating and analyzing the monitoring data of the transmission line monitoring group 2 and the transmission tower monitoring group 3, evaluating the posture of the current transmission tower and transmission line, and sending the current transmission tower and transmission line status data and warning level to the data display platform 4;

[0085] The data display platform 4 is used to display the current status of the transmission tower and the transmission line, and to show whether there is a status alarm.

[0086] In this embodiment, the line sensors include a conductor temperature monitoring sensor 21, a conductor dancing monitoring sensor 22, a tension clamp temperature monitoring sensor 23, and a conductor current monitoring sensor 24; the pole tower monitoring group 3 includes a pole tower tilt monitoring sensor module 31, a micro-meteorological monitoring sensor 32, and a monitoring camera 33, and the micro-meteorological monitoring sensor 32 is a seven-element micro-meteorological sensor.

[0087] In this embodiment, the sensors and monitoring cameras 33 in the line monitoring group 2 and the pole tower monitoring group 3 all include a Beidou positioning module, which is used to perform Beidou satellite time calibration on all sensors and monitoring cameras 33 to keep all sensors and monitoring cameras 33 in the same clock system.

[0088] like Figure 2 As shown, in this embodiment, the tower inclination monitoring sensor module 31 includes a processor 310, a millimeter wave radar module 311 responsible for collecting the distance from the device to the ground, a first six-axis acceleration sensor 312 responsible for collecting the three-axis acceleration and three-axis angular velocity of the device, a barometer module 313 responsible for collecting atmospheric pressure, a high-precision positioning module 314 responsible for collecting longitude and latitude data and altitude, a three-axis geomagnetic module 315 responsible for collecting geomagnetic declination data, and a charge and discharge management system 316 responsible for collecting the charging current, charging voltage, discharge current and battery voltage of the device. The charge and discharge management system 316 is also electrically connected to a photovoltaic panel 317 and a lithium battery pack 318.

[0089] like Figure 3 As shown, in this embodiment, the monitoring camera 33 is securely installed with the hole of the transmission tower itself through a specific angle steel mounting bracket, a locking nut and a threaded adhesive; if the bracket for installing the monitoring camera 33 or the monitoring camera itself is offset, resulting in an early warning caused by a non-tilted settlement problem in the image, the user can recalibrate the monitoring camera 33 in the user background, and the monitoring camera 33 includes a camera processor module 331, an AI module 332, a 4G module 333, a wireless communication module 334 and a second six-axis acceleration sensor 335; the monitoring camera 33 uses the wireless communication module 334 to collect data from the wireless tower tilt sensor module, combines the on-site image, the internal second six-axis acceleration sensor 335 and the tower tilt monitoring sensor module 31 data inside the camera processor module 331, uses the AI ​​module 332 to perform deep learning calculations, uploads the on-site data to the integrated monitoring front end 1 and sends the analysis results to the data display platform 4; this design combines the fault detection technology of the AI ​​algorithm and image analysis, which can significantly improve the efficiency and accuracy of fault identification. Through automated testing processes, companies can reduce labor costs, improve production efficiency, and reduce losses caused by equipment failures. With the continuous advancement of technology, future fault detection systems will be more intelligent and efficient, providing stronger support for all walks of life.

[0090] In this embodiment, the processor 310 uses the SPI interface to collect the current atmospheric pressure from the barometer module 313, and uses the formula h=(p0-p) / k, where h is the height (meter), p0 is the sea level pressure (Pascal), p is the current air pressure (Pascal), and k is a constant of air pressure changing with height, usually about 12 meters / millibar (or 133 Pa) within 3000 meters above sea level. By comparing with the installation calibrated height, the relative settlement height of the transmission tower can be calculated; wherein the processor 310 uses the IIC interface to collect the three-axis geomagnetic module 315 to read the magnetic field strength in the three-axis direction, and the magnetic field strength is then converted into a heading angle through an algorithm formula, and compared with the initial magnetic declination angle of the installation calibration, the relative horizontal torsion angle of the transmission tower can be calculated, which can reflect whether the transmission tower is tilted; wherein the high-precision positioning module 314 supports single Beidou positioning and Beidou-3 positioning systems, and the processor 310 uses the TTL interface to access the high-precision positioning module 314, and uses the wireless communication module 334 obtains differential data from Qianxun Location Company from the monitoring camera 33 at a frequency of 1 Hz, and sends it to the high-precision positioning module 314 through the RTCM3.2 protocol, thereby obtaining centimeter-level Beidou positioning data, including longitude and latitude data and altitude data. By comparing with the initial longitude and latitude and altitude calibrated by the installation, the relative horizontal displacement and settlement height of the transmission tower can be calculated respectively; using the second six-axis acceleration sensor 335, the camera processor module 331 obtains the original three-axis acceleration value and three-axis angular velocity value, and uses the complementary filtering algorithm and the Kalman filtering algorithm to solve the device attitude angle. The attitude angle is expressed by Euler angles, and compared with the installation calibrated inclination angle, the relative inclination angle of the transmission tower can be obtained; the camera processor module 331 is connected to the AI ​​module 332, and the camera processor module 331 arrives at the specified preset position to obtain the on-site picture, and combines the AI ​​module 332 to perform deep learning analysis to obtain the result, and it can be judged whether the transmission tower is tilted based on the current result.

[0091] In this embodiment, the pole tower inclination monitoring sensor module 31 is installed on the transmission pole tower in an independent installation mode. The millimeter wave radar module 311 is installed face down without obstruction. The wireless communication module 334 is connected to the monitoring camera 33 using 2.4G wireless micro-power self-organizing network technology for data communication. The connection is convenient and has strong anti-interference ability. The wireless connection method can avoid the installation limitations caused by wired connection, and is easy to adapt to different positions of three-phase multi-split conductors, thereby realizing the function of multi-point monitoring and comprehensive comparison.

[0092] The monitoring method includes performing Beidou satellite time calibration on all sensors and monitoring cameras 33 through a Beidou positioning module, and then performing comprehensive temperature judgment monitoring on the transmission line and tilt settlement monitoring on the transmission tower pole, using all-weather real-time monitoring sensors to replace manual inspections, and realizing regular and quantitative monitoring of transmission towers and transmission lines. The digital display method determines the early warning level of the transmission tower line in a step-by-step manner, and divides the monitoring results into five early warning levels, namely, level 1: normal status; level 2: attention; level 3: close attention; level 4: general abnormality; level 5: serious abnormality; and the data display platform 4 will display the specific content of the early warning level.

[0093] like Figure 4 As shown, in the present embodiment, the Beidou satellite time calibration is based on the Beidou satellite clock system, the above-mentioned sensor data acquisition message includes a timestamp, the integrated monitoring front end receives the sensor message, verifies and records the timestamp of the data, and the above-mentioned sensor message format: message header (1 byte) + timestamp (4 bytes, century seconds) + monitoring data + check bit (2 bytes, CRC16-MODBUS) + message tail (1 byte); wherein the integrated front-end device receives the message sent by the sensor, verifies the timestamp, if the timestamp in the sensor message differs from its own time by more than or equal to 3 seconds, the current monitoring data is discarded and the time correction logic is performed; if the difference is less than 3 seconds, it is considered that the time is normal, and the timestamp and monitoring data in the message are saved for subsequent comprehensive analysis logic use;

[0094] Input the time t1 of the integrated monitoring front-end 1 device and the time t2 in the monitoring message, take t1 and t2 as the initial values, establish the natural time clocks T1 and T2, which are used for subsequent time comparison, introduce the standard clock source and create the natural time clock T3 through the timing function of the Beidou positioning module, compare T1 and T2 with T3 respectively, if the difference between the clock T1 of the integrated monitoring front-end 1 device and the standard clock T3 is greater than or equal to 3 seconds, the clock of the integrated monitoring front-end 1 device is abnormal, and the standard clock T3 is used for correction, if the difference between the monitoring message clock T2 and the standard clock is greater than or equal to 3 seconds, the sensor clock is abnormal, and the integrated monitoring front-end 1 device will send instructions to the sensor to let it calibrate the time through the Beidou positioning module;

[0095] When the clock correction is completed, the integrated monitoring front-end 1 device sends a command to the sensor, and the sensor needs to re-send the monitoring data.

[0096] like Figure 5 As shown, in this embodiment, the comprehensive judgment and monitoring method for the temperature of a transmission line includes the following steps:

[0097] A1. The temperature of the transmission line is collected by the conductor temperature monitoring sensor 21, the temperature of the tension clamp is collected by the tension clamp temperature monitoring sensor 23, the transmission line current is collected by the conductor current monitoring sensor 24, and the ambient temperature and light radiation intensity are collected by the micro-meteorological monitoring sensor 32. The data integration analysis is processed in the integrated monitoring front end 1;

[0098] A2. Set the temperature alarm threshold to 45°C. When the tension clamp temperature exceeds the limit alarm, the following process will be used for judgment:

[0099] A21. If the temperature of the tension clamp is greater than 45°C, set the warning level to Level 2, Concern;

[0100] A22. Sample the temperature data of the transmission line. If the temperature data of the transmission line is less than or equal to 45°C, the temperature of the tension clamp alone is abnormally high. It is judged that the tension clamp is abnormal and needs to be checked urgently. The warning status is set to level 4;

[0101] A23. If the temperature data of the transmission line is greater than 45°C, the transmission line current and ambient temperature data are sampled, and the theoretical value of temperature change is calculated based on the axial line heat dissipation model. If the theoretical value of temperature change is greater than 0, it is determined that the line temperature has increased due to the line current and will continue to increase, and the warning level is set to level 2. If the theoretical value of temperature change is less than or equal to 0, the line temperature change is unrelated to the line current, and the next step is to make a judgment based on the light radiation intensity.

[0102] The above wire heat dissipation model is as follows:

[0103]

[0104] in

[0105] ΔT: temperature change; I: conductor current; ρ: conductor resistivity; α: conductor resistance temperature coefficient; θ: conductor temperature; K t : comprehensive heat dissipation coefficient; L: conductor cross-sectional perimeter; S: conductor cross-sectional area;

[0106] If the light radiation intensity is greater than 100W / m2, the light radiation intensity will affect the temperature changes of the conductor and the tension clamp temperature. The warning level is set to level 3 and close attention is required. If the light radiation intensity is not greater than 100W / m2, the temperature change may be caused by discharge caused by foreign objects hanging on the conductor, etc. The warning level is set to level 4 and the abnormality is reported.

[0107] like Figure 6As shown, in this embodiment, the warning classification mechanism used in the wire dancing monitoring application scenario is: the dancing amplitude, wire breakage mark, and deicing jump state collected by the wire dancing monitoring sensor are used to perform wire dancing warning classification; if the wire breakage mark is effective, it is set to the highest level 5. Then the dancing amplitude is judged. If the dancing amplitude is greater than or equal to 20cm and less than 40cm, it is set to level 2. If the dancing amplitude is greater than or equal to 40cm and less than 100cm, it is set to level 3. If the dancing amplitude is greater than 100cm, it is set to level 4. On this basis, the deicing jump state judgment is added. If the deicing jump state is effective, the warning level is increased by one level on the original basis, and the maximum does not exceed level 5. The deicing jump state is verified using the wire temperature. If the wire temperature is greater than 5°C, the ice covering state is not effective.

[0108] like Figure 7 As shown, in this embodiment, the wire dancing pre-warning judgment logic is: when the wire dancing monitoring sensor does not generate an early warning, a comprehensive analysis of four factors, namely, ambient temperature, ambient humidity, wire temperature and wind speed, is performed to determine whether the dancing pre-condition can be met and form an early warning signal. The first step is to determine whether the ambient temperature meets the low temperature condition. The second step is to determine whether the ambient humidity meets the condition (ambient humidity or rainfall). The next step is to determine whether the wire temperature meets the condition (perform a secondary check on the ambient temperature in the first step). If all of the above conditions are met, the ice covering pre-condition is met, and the early warning level is set to level 2. The wind speed is determined when the ice covering condition is met. If the wind speed is greater than level 5 (10.8m / s), the wire dancing pre-condition is met, and the early warning level is set to level 3, and close attention is paid.

[0109] In this embodiment, the method for monitoring the inclination and settlement of a transmission tower includes the following steps:

[0110] S1. Set the tower tilt settlement monitoring cycle and send it to the monitoring camera 33. The monitoring camera 33 is in standby mode most of the time. When the monitoring cycle time arrives, the monitoring camera 33 first sends the tilt settlement monitoring instruction to the tower tilt monitoring sensor module 31 through the wireless communication module 334. The tower tilt monitoring sensor module 31 and the monitoring camera 33 simultaneously collect and analyze the monitoring data of the transmission tower tilt settlement. The specific steps are as follows:

[0111] S11. Start the initialization of the second six-axis acceleration sensor 335, collect three-axis acceleration and three-axis angular velocity for 10 cycles, and use the complementary filtering algorithm and the Kalman filtering algorithm to calculate the attitude angle of the collected data to obtain the current attitude angle. The overall error percentage formula for the attitude angle is as follows:

[0112] Overall attitude angle error = |X current - X initial / user preset maximum offset value of X-axis inclination | + |Y current - Y initial / user preset maximum offset value of Y-axis inclination | / 2*100

[0113] Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default;

[0114] S12. The monitoring camera 33 controls the camera processor module 331 to run to the calibration preset position and collects a static picture. The algorithms used are all open source algorithms, which are as follows: the AI ​​module 332 uses the VGG neural network pre-trained at the factory to extract the features of the collected static picture, and compares the calibration picture features with the features through the cosine similarity method to obtain the non-similarity rate between the two, thereby obtaining the error percentage of the picture feature quantity; the overall error of the tilt settlement of the monitoring camera 33 can be obtained by the following formula:

[0115] The overall error of tilt and settlement of monitoring camera 33 = K1* overall error of attitude angle + K2* error of image feature quantity

[0116] Among them, K1 is the weight of the overall error of the attitude angle, accounting for 80%, K2 is the weight of the error of the image feature quantity, accounting for 20%, K1+K2=1; in the system of monitoring camera 33, the monitoring of the attitude angle is the most effective in reflecting whether the transmission tower is tilted or subsided, and the error of the image feature quantity is used to assist in monitoring whether the transmission tower is subsided;

[0117] S2. The first step of the pole tower tilt monitoring sensor module 31 is to start the first six-axis acceleration sensor 312 to collect 10 cycles of three-axis acceleration and three-axis angular velocity, and use the complementary filtering algorithm and the Kalman filtering algorithm to solve the attitude angle of the collected data to obtain the current attitude angle of the pole tower tilt monitoring sensor module 31. The overall error percentage formula of the attitude angle of the pole tower tilt monitoring sensor module 31 is calculated as follows:

[0118] Overall attitude angle error = |X current - X initial / user preset maximum offset value of X-axis inclination | + |Y current - Y initial / user preset maximum offset value of Y-axis inclination | / 2*100

[0119] Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default;

[0120] The second step is to start the millimeter wave radar module 311 for uninterrupted measurement, collect the ground distance of 10 cycles, add the Kalman filter algorithm to obtain the current ground distance of the tower tilt monitoring sensor module 31, and calculate the error percentage formula of the ground distance of the tower tilt monitoring sensor module 31 as follows:

[0121] Ground distance error = |Current ground distance - Initial ground distance / User preset maximum ground distance error value | * 100

[0122] The maximum value of the ground distance error preset by the user is 10 cm by default;

[0123] The third step is to enable the barometer module 313 to collect the atmospheric pressure for 5 cycles, and use the mean algorithm to calculate the atmospheric pressure at the height of the pole tower tilt monitoring sensor module 31, and then use the formula h=p0-p / k to calculate the current vertical height of the pole tower tilt monitoring sensor module 31. The formula for calculating the vertical height error percentage of the pole tower tilt monitoring sensor module 31 is as follows:

[0124] Vertical height error = |Current vertical height - Initial vertical height / Maximum vertical height error preset by the user| * 100

[0125] The maximum vertical height error preset by the user is 10 cm by default;

[0126] The fourth step is to enable the three-axis geomagnetic module 315 to read the magnetic field strength in the three directions of X, Y, and Z, and use the formula to calculate the heading angle. The previously calculated heading angle is then converted into degrees through the formula. The converted degrees are the current heading angle of the tower tilt monitoring sensor module 31. The formula for calculating the heading angle error percentage of the tower tilt monitoring sensor module 31 is as follows:

[0127] Heading angle error = |current heading angle - initial heading angle / maximum heading angle error preset by the user | * 100

[0128] The default maximum value of the heading angle error preset by the user is 5 degrees;

[0129] The fifth step is to enable the high-precision positioning module 314 and wait for the high-precision positioning module 314 to complete the cold start and enter the positioning state. The single-point positioning is completed by acquiring multiple Beidou satellite signals through the high-precision antenna. The data packet containing the BDGGA data frame required by Qianxun Position is forwarded to the monitoring camera 33 through the wireless communication module 334. The monitoring camera 33 successfully authenticates with the Qianxun Position background and obtains the differential data packet of the RTCM3.2 protocol, which is forwarded to the processor of the pole tower tilt monitoring sensor module 31 through the wireless communication module 334 at a frequency of 1Hz. The processor then forwards it to the high-precision positioning module 314 using the TTL interface. The high-precision positioning module 314 performs differential centimeter-level positioning and uses the Kalman filter algorithm to filter the longitude and latitude and altitude data to obtain higher-precision longitude and altitude data; the longitude and latitude and altitude data of this positioning are used as the current longitude and latitude and altitude of the pole tower tilt monitoring sensor module 31; the formula for calculating the error percentage of the longitude and latitude altitude of the pole tower tilt monitoring sensor module 31 is as follows:

[0130] Earth radius: The average radius of the earth is usually taken as R, which is 6371 km. The longitude and latitude are converted into radians:

[0131] lat1=radians(lat1),lon1=radians(lon1)

[0132] Lat2=radians(lat2),lon2=radians(lon2)

[0133] Use the following formula to calculate the current high-precision positioning and initial high-precision positioning three-dimensional space coordinates (x, y, z):

[0134] x=(R+h)×cos(lat)×cos(lon)

[0135] y=(R+h)×cos(lat)×sin(lon)

[0136] z=(R+h)×sin(lat)

[0137] where h is the height above sea level.

[0138] 3D distance calculation

[0139] After obtaining the three-dimensional coordinates of the current high-precision positioning and the initial high-precision positioning, use the three-dimensional space distance formula:

[0140]

[0141] Latitude and longitude altitude error = |spatial distance d - maximum value of user preset safety distance / maximum value of user preset safety distance | * 100

[0142] The maximum value of the user-preset safety distance is 10 cm by default

[0143] The final result is that the overall error of the tilt settlement of the tower tilt monitoring sensor module 31 can be obtained by the following formula:

[0144] The total error of the tilt settlement of the tower tilt monitoring sensor module 31 = L1* overall error of attitude angle + L2* distance to the ground error + L3* vertical height error + L4* heading angle error + L5* latitude and longitude altitude error

[0145] Among them, L1 is the weight of the overall attitude angle error, accounting for 51%, L2 is the weight of the error of the distance to the ground, accounting for 9%, L3 is the weight of the vertical height error, accounting for 9%, L4 is the weight of the heading angle error, accounting for 5%, L5 is the weight of the longitude, latitude and altitude error, accounting for 26%, and L1 + L2 + L3 + L4 + L5 = 1; Through self-test experiments combined with the sensor usage environment, it is obtained that the attitude angle has a higher accuracy in monitoring the inclination of transmission towers than other parameters. Secondly, it is the longitude, latitude and altitude. The distance to the ground, the vertical height and the heading angle play an auxiliary monitoring role;

[0146] S3. The tower inclination monitoring sensing module 31 reports the overall inclination and settlement error result to the monitoring camera 33 through the wireless communication module 334. The monitoring camera 33 collates its own overall inclination and settlement error and uploads it to the integrated monitoring front end 1 through the 4G module 333. The integrated monitoring front end 1 calculates the warning level of the transmission tower according to the preset weight ratio. The calculation method is as follows:

[0147] The inclination and settlement error E of the transmission tower = J1 * the overall inclination and settlement error of the monitoring camera 33 + J2 * the overall inclination and settlement error of the tower inclination monitoring sensing module 31

[0148] Among them, J1 is the weight of the monitoring camera 33, accounting for 28%, J2 is the weight of the tower inclination monitoring sensing module 31, accounting for 72%, and J1 + J2 = 1; In this method, the weight ratio is such that the monitoring of the inclination and settlement of the transmission tower is mainly completed by the wireless tower sensor, and the monitoring camera 33 plays an auxiliary monitoring role. The weight ratio is obtained from laboratory environment tests; Among them, when 0 < E ≤ 1, it is in a normal state; 1 < E ≤ 2 is level 1, 2 < E ≤ 3 is level 2; 3 < E ≤ 4 is level 3; 4 < E ≤ 5 is level 4; E > 5 is level 5.

[0149] Although the embodiments of the present invention are described in terms of actual solutions, they do not constitute a limitation to the meaning of the present invention. For those skilled in the art, modifications to its implementation solutions according to this specification and combinations with other solutions are obvious.

Claims

1. A Beidou-based transmission tower line integrated monitoring device and monitoring method, characterized in that: It includes an integrated monitoring front end (1), a line monitoring group (2), a tower monitoring group (3) and a data display platform (4); The line monitoring group (2) is used to perform all-round monitoring of the power transmission line, and is composed of a number of line sensors for monitoring the state of the power transmission line; The tower monitoring group (3) is used to monitor the posture of the transmission tower itself and the tower environmental factors; The integrated monitoring front end (1) is responsible for integrating and analyzing the monitoring data of the transmission line monitoring group (2) and the transmission tower monitoring group (3), evaluating the posture of the current transmission tower and transmission line, and sending the current transmission tower and transmission line status data and warning level to the data display platform (4); The data display platform (4) is used to display the current status of the transmission tower and the transmission line, and to show whether there is a status alarm.

2. According to claim 1, a Beidou-based transmission tower line integrated monitoring device is characterized in that: The line sensors include a conductor temperature monitoring sensor (21), a conductor dance monitoring sensor (22), a tension clamp temperature monitoring sensor (23), and a conductor current monitoring sensor (24); the pole tower monitoring group (3) includes a pole tower tilt monitoring sensor module (31) and a micro-meteorological monitoring sensor (32) and a monitoring camera (33); the micro-meteorological monitoring sensor (32) is a seven-element micro-meteorological sensor.

3. The Beidou-based transmission tower line integrated monitoring device according to claim 2, characterized in that: The sensors and monitoring cameras (33) in the line monitoring group (2) and the pole tower monitoring group (3) all include a Beidou positioning module, and the Beidou positioning module is used to perform Beidou satellite time calibration on all sensors and monitoring cameras (33) to keep all sensors and monitoring cameras (33) in the same clock system.

4. The Beidou-based transmission tower line integrated monitoring device according to claim 3 is characterized in that: The pole tower tilt monitoring sensor module (31) comprises a millimeter wave radar module (311) responsible for collecting the distance between the device and the ground, a first six-axis acceleration sensor (312) responsible for collecting the three-axis acceleration and three-axis angular velocity of the device, a barometer module (313) responsible for collecting atmospheric pressure, a high-precision positioning module (314) responsible for collecting longitude and latitude data and altitude, a three-axis geomagnetic module (315) responsible for collecting geomagnetic declination data, and a charge and discharge management system (316) responsible for collecting the charging current, charging voltage, discharge current and battery voltage of the device.

5. The Beidou-based transmission tower line integrated monitoring device according to claim 4 is characterized in that: The monitoring camera (33) comprises a camera processor module (331), an AI module (332), a 4G module (333), a wireless communication module (334) and a second six-axis acceleration sensor (335); the monitoring camera (33) uses the wireless communication module (334) to collect data from the pole tower tilt monitoring sensor module (31), combines the on-site image, the second six-axis acceleration sensor (335) and the pole tower tilt monitoring sensor module (31) data inside the camera processor module (331), uses the AI ​​module (332) to perform deep learning calculations, uploads the on-site data to the integrated monitoring front end (1), and sends the analysis results to the data display platform (4).

6. A monitoring method comprising the Beidou-based transmission tower line integrated monitoring device according to claim 5, characterized in that: It includes Beidou satellite time calibration for all sensors and monitoring cameras (33) through Beidou positioning module, and then comprehensive temperature judgment monitoring of transmission lines and tilt settlement monitoring of transmission towers, and divides the monitoring results into five warning levels, namely, level 1: normal state; level 2: attention; level 3: close attention; level 4: general abnormality; Level 5: Severe abnormality; The comprehensive judgment and monitoring method for the temperature of a transmission line includes the following steps: A1. The temperature of the transmission line is collected by the conductor temperature monitoring sensor (21), the temperature of the tension clamp is collected by the tension clamp temperature monitoring sensor (23), the current of the transmission line is collected by the conductor current monitoring sensor (24), and the ambient temperature and light radiation intensity are collected by the micro-meteorological monitoring sensor (32). The data integration analysis is processed in the integrated monitoring front end (1); A2. Set the temperature alarm threshold to 45°C. When the tension clamp temperature exceeds the limit alarm, the following process will be used for judgment: A21. If the temperature of the tension clamp is greater than 45°C, set the warning level to Level 2, Concern; A22. Sample the temperature data of the transmission line. If the temperature data of the transmission line is less than or equal to 45°C, the temperature of the tension clamp alone is abnormally high. It is judged that the tension clamp is abnormal and needs to be checked urgently. The warning status is set to level 4; A23. If the temperature data of the transmission line is greater than 45°C, the transmission line current and ambient temperature data are sampled, and the theoretical value of temperature change is calculated based on the axial line heat dissipation model. If the theoretical value of temperature change is greater than 0, it is determined that the line temperature has increased due to the line current and will continue to increase, and the warning level is set to level 2. If the theoretical value of temperature change is less than or equal to 0, the line temperature change is unrelated to the line current, and the next step is to make a judgment based on the light radiation intensity. The above wire heat dissipation model is as follows: in ΔT: temperature change; I: conductor current; ρ: conductor resistivity; α: conductor resistance temperature coefficient; θ: conductor temperature; K t : comprehensive heat dissipation coefficient; L: conductor cross-sectional perimeter; S: conductor cross-sectional area; If the light radiation intensity is greater than 100W / m2, the light radiation intensity will affect the temperature changes of the conductor and the tension clamp temperature. The warning level is set to level 3 and close attention is required. If the light radiation intensity is not greater than 100W / m2, the temperature change may be caused by discharge caused by foreign objects hanging on the conductor, etc. The warning level is set to level 4 and the abnormality is reported.

7. The monitoring method according to claim 6, characterized in that: The Beidou satellite time calibration is based on the Beidou satellite clock system. The above-mentioned sensor data collection message contains a timestamp. The integrated monitoring front end (1) receives the sensor message, verifies and records the timestamp of the data. The above-mentioned sensor message format is: message header (1 byte) + timestamp (4 bytes, century seconds) + monitoring data + check bit (2 bytes, CRC16-MODBUS) + message tail (1 byte); wherein the integrated front end device receives the message sent by the sensor, verifies the timestamp, and if the difference between the timestamp in the sensor message and the sensor time is greater than or equal to 3 seconds, the current monitoring data is discarded and the time correction logic is performed; if the difference is less than 3 seconds, it is considered that the time is normal, and the timestamp and monitoring data in the message are saved for subsequent comprehensive analysis logic use; Input the time t1 of the integrated monitoring front end (1) device and the time t2 in the monitoring message, take t1 and t2 as the initial values, establish the natural time clocks T1 and T2, which are used for subsequent time comparison, introduce the standard clock source and create the natural time clock T3 through the timing function of the Beidou positioning module, compare T1 and T2 with T3 respectively, if the difference between the clock T1 of the integrated monitoring front end (1) device and the standard clock T3 is greater than or equal to 3 seconds, the clock of the integrated monitoring front end (1) device is abnormal, and the standard clock T3 is used for correction, if the difference between the monitoring message clock T2 and the standard clock is greater than or equal to 3 seconds, the sensor clock is abnormal, and the integrated monitoring front end (1) device will send a command to the sensor to let it calibrate the time through the Beidou positioning module; When the clock calibration is completed, the integrated monitoring front-end (1) device sends a command to the sensor, and the sensor needs to re-send the monitoring data.

8. The monitoring method according to claim 7, characterized in that: The method for monitoring the inclination and settlement of a transmission tower comprises the following steps: S1. Set the tower tilt settlement monitoring cycle and send it to the monitoring camera (33). The monitoring camera (33) is in standby mode most of the time. When the monitoring cycle time arrives, the monitoring camera (33) first sends the tilt settlement monitoring instruction to the tower tilt monitoring sensor module (31) through the wireless communication module (334). The tower tilt monitoring sensor module (31) and the monitoring camera (33) simultaneously collect and analyze the monitoring data of the transmission tower tilt settlement. The specific steps are as follows: S11. Start the initialization of the second six-axis acceleration sensor (335), collect three-axis acceleration and three-axis angular velocity of 10 cycles, and use complementary filtering algorithm and Kalman filtering algorithm to calculate the attitude angle of the collected data to obtain the current attitude angle. The overall error percentage of the attitude angle is calculated as follows: Overall attitude angle error = (|(X(current)-X(initial)) / (user preset maximum offset value of X-axis tilt angle)|+|(Y(current)-Y(initial)) / (user preset maximum offset value of Y-axis tilt angle)|) / 2*100 Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default. S12. The monitoring camera (33) controls the camera processor module (331) to run to the calibration preset position, collects a static picture, uses the AI ​​module (332) to use the VGG neural network pre-trained at the factory to extract the features of the collected static picture, compares the calibration picture features with the calibration picture features by the cosine similarity method, and obtains the non-similarity rate between the two, thereby obtaining the error percentage of the picture feature quantity; the overall tilt settlement error of the monitoring camera (33) can be obtained by the following formula: Monitoring camera (33) tilt and settlement overall error = K1* overall attitude angle error + K2* image feature error Wherein K1 is the weight of the overall error of the attitude angle, K2 is the weight of the error of the image feature quantity, and K1+K2=1; in the monitoring camera (33) system, the monitoring of the attitude angle can best reflect whether the transmission tower is tilted or subsided, and the error of the image feature quantity is used to assist in monitoring whether the transmission tower is subsided; S2. The first step of the tower tilt monitoring sensor module (31) is to start the first six-axis acceleration sensor (312) to collect 10 cycles of three-axis acceleration and three-axis angular velocity, and use the complementary filtering algorithm and the Kalman filtering algorithm to solve the attitude angle of the collected data to obtain the current attitude angle of the tower tilt monitoring sensor module (31), and calculate the overall error percentage of the attitude angle of the non-tower tilt monitoring sensor module (31) as follows: Overall attitude angle error = (|(X(current)-X(initial)) / (user preset maximum offset value of X-axis tilt angle)|+|(Y(current)-Y(initial)) / (user preset maximum offset value of Y-axis tilt angle)|) / 2*100 Where X and Y are the attitude angles in the X-axis and Y-axis directions respectively. The maximum offset value of the X-axis tilt angle preset by the user and the maximum offset value of the Y-axis tilt angle preset by the user are 5 degrees by default. The second step is to start the millimeter wave radar module (311) to perform uninterrupted measurement, collect the ground distance of 10 cycles, add the Kalman filter algorithm to obtain the current ground distance of the tower tilt monitoring sensor module (31), and calculate the ground distance error percentage formula of the tower tilt monitoring sensor module (31) as follows: Ground distance error = |((current ground distance)-(initial ground distance)) / (user preset maximum ground distance error)|*100 The maximum value of the ground distance error preset by the user is 10 cm by default; The third step is to enable the barometer module (313) to collect the atmospheric pressure of 5 cycles, and use the mean algorithm to calculate the atmospheric pressure at the height of the pole tower tilt monitoring sensor module (31), and then use the formula h=(p0-p) / k to calculate the current vertical height of the pole tower tilt monitoring sensor module (31). The formula for calculating the vertical height error percentage of the pole tower tilt monitoring sensor module (31) is as follows: Vertical height error = |((current vertical height)-(initial vertical height)) / (maximum vertical height error preset by the user)|*100 The maximum vertical height error preset by the user is 10 cm by default; The fourth step is to enable the three-axis geomagnetic module (315) to read the magnetic field strength in the three directions of X, Y, and Z, and use the formula to calculate the heading angle. The previously calculated heading angle is then converted into degrees through the formula. The converted degrees are the current heading angle of the pole tower tilt monitoring sensor module (31). The formula for calculating the heading angle error percentage of the pole tower tilt monitoring sensor module (31) is as follows: Heading angle error = |((current heading angle)-(initial heading angle)) / (user preset maximum heading angle error)|*100 The default maximum value of the heading angle error preset by the user is 5 degrees; The fifth step is to enable the high-precision positioning module (314) and wait for the high-precision positioning module (314) to complete the cold start and enter the positioning state. The high-precision antenna obtains multiple Beidou satellite signals to complete single-point positioning. The wireless communication module (334) forwards the data packet containing the BDGGA data frame required by Qianxun Location to the monitoring camera (33). The monitoring camera (33) successfully authenticates with the Qianxun Location background and obtains the differential data packet of the RTCM3.2 protocol, which is forwarded to the tower tilt through the wireless communication module (334) at a frequency of 1 Hz. The processor of the monitoring sensor module (31) forwards the data to the high-precision positioning module (314) by using the TTL interface. The high-precision positioning module (314) performs differential centimeter-level positioning and uses the Kalman filter algorithm to filter the longitude, latitude and altitude data, thereby obtaining longitude, latitude and altitude data with higher accuracy. The longitude, latitude and altitude data of this positioning are used as the current longitude, latitude and altitude of the pole tower tilt monitoring sensor module (31). The formula for calculating the error percentage of the longitude, latitude and altitude of the pole tower tilt monitoring sensor module (31) is as follows: Earth radius: The average radius of the earth is usually taken as 6371 km, and the longitude and latitude are converted to radians: lat1 = radians (lat1), lon1 = radians (lon1) Lat2=radians(lat2),lon2=radians(lon2) Use the following formula to calculate the current high-precision positioning and initial high-precision positioning three-dimensional space coordinates (x, y, z): x=(R+h)×cos(lat)×cos(lon) y=(R+h)×cos(lat)×sin(lon) z=(R+h)×sin(lat) Where h is the height (altitude). 3D distance calculation After obtaining the three-dimensional coordinates of the current high-precision positioning and the initial high-precision positioning, use the three-dimensional space distance formula: Latitude and longitude altitude error = |((spatial distance d)-(user preset maximum safety distance)) / (user preset maximum safety distance)|*100 The default maximum safety distance is 10 cm. The final result is that the overall error of the tilt settlement of the tower tilt monitoring sensor module (31) can be obtained by the following formula: The total error of the tilting and settling of the tower tilt monitoring sensor module (31) = L1*total error of attitude angle + L2*ground distance error + L3*vertical height error + L4*heading angle error + L5*latitude and longitude altitude error Among them, L1 is the weight of the overall error of the attitude angle, L2 is the weight of the error of the distance to the ground, L3 is the weight of the vertical height error, L4 is the weight of the heading angle error, and L5 is the weight of the error of the latitude and longitude altitude. L1+L2+L3+L4+L5=1. After self-test experiments combined with the sensor use environment, it is concluded that the attitude angle has a higher accuracy in monitoring the tilt of the transmission tower than other parameters, followed by the latitude and longitude altitude. The distance to the ground, vertical height and heading angle are auxiliary monitoring functions. S3. The pole tilt monitoring sensing module (31) reports the overall tilt settlement error result to the monitoring camera (33) through the wireless communication module (334). The monitoring camera (33) collates its own overall tilt settlement error and uploads it to the integrated monitoring front end (1) through the 4G module (333). The integrated monitoring front end (1) calculates the warning level of the transmission pole tower according to the preset weight ratio. The calculation method is as follows: The tilt settlement error E of the transmission pole tower = J1 * the overall tilt settlement error of the monitoring camera (33) + J2 * the overall tilt settlement error of the pole tilt monitoring sensing module (31) Where J1 is the weight of the monitoring camera (33), J2 is the weight of the pole tilt monitoring sensing module (31), and J1 + J2 = 1; in this method of weight ratio, the monitoring of the tilt settlement of the transmission pole tower is mainly completed by the wireless pole sensor, and the monitoring camera (33) plays an auxiliary monitoring role. The weight ratio is obtained from the laboratory environment test; where 0 < E ≤ 1 is the normal state; 1 < E ≤ 2 is level 1, 2 < E ≤ 3 is level 2; 3 < E ≤ 4 is level 3; 4 < E ≤ 5 is level 4; E > 5 is level 5.

9. The monitoring method according to claim 7, characterized in that: The proportion of K1 is 80%, and the proportion of K2 is 20%; the proportion of L1 is 51%, the proportion of L2 is 9%, the proportion of L3 is 9%, the proportion of L4 is 5%, the proportion of L5 is 26%; the proportion of J1 is 28%, and the proportion of J2 is 72%.

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